Exploring Mediatoil Imagery: A Content-Based Approach

Description

The future of Alberta’s bitumen sands, also known as “oil sands” or “tar sands,” and their place in Canada’s energy future has become a topic of much public debate. Within this debate, the print, television, and social media campaigns of those who both support and oppose developing the oil sands are particularly visible. As such, campaigns around the oil sands may be seen as influencing audience perceptions of the benefits and drawbacks of oil sands production. There is consequently a need to study the media materials of various tar sands stakeholders and explore how they differ. In this setting, it is essential to gather documents and identify content within images, which requires the use of an image retrieval technique such as a content-based image retrieval (CBIR) system. In a CBIR system, images are represented by low-level features (i.e. specific structures in the image such as points, edges, or objects), which are used to distinguish pictures from one another.
The oil sands domain has to date not been mapped using CBIR systems. The research thus focuses on creating an image retrieval system, namely Mediatoil-IR, for exploring documents related to the oil sands. Our aim is to evaluate various low-level representations of the images within this context. To this end, our experimental framework employs LAB color histogram (LAB) and speeded up robust features (SURF) in order to typify the imagery. We further use machine learning techniques to improve the quality of retrieval (in terms of both accuracy and speed). To achieve this aim, the extracted features from each image are encoded in the form of vectors and used as a training set for learning classification models to organize pictures into different categories. Different algorithms were considered such as Linear SVM, Quadratic SVM, Weighted KNN, Decision Trees, Bagging, and Boosting on trees. It was shown that Quadratic SVM algorithm trained on SURF features is a good approach for building CBIR, and is used in building Mediatoil-IR.
Finally, with the help of created CBIR, we were able to extract the similar documents and explore the different types of imagery used by different stakeholders. Our experimental evaluation shows that our Mediatoil-IR system is able to accurately explore the imagery used by different stakeholders.